Sr. Knowledge Platform Architect - Claims & Ops
About this role
Sr Cons Learning - TD07FE
We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the
future.
The Sr. Knowledge Platform Architect is a senior role responsible for defining and driving the end-to-end knowledge technology strategy, including headless content management architecture, content modeling, semantic structure (taxonomy/ontology/metadata), and AI integration—to ensure enterprise knowledge assets are AI-ready, governed, and operationally scalable. This role partners closely with Data Science, AI Engineering, Enterprise Architecture, and Knowledge Management to enable reliable knowledge retrieval, reasoning, and automation across AI use cases.
This individual is also accountable for upskilling and coaching the Knowledge Team in modern headless CMS patterns and structured content modeling, building durable internal capability while influencing stakeholders who may be unfamiliar with (or resistant to) new approaches.
The ideal candidate brings a unique blend of technical product leadership, content architecture expertise, AI governance acumen, and cross-functional influence to accelerate the organization's knowledge modernization journey.
WORK ARRANGEMENTS
:
This role can have a Hybrid or Remote work schedule. Candidates who live near one of our offices will have the expectation of working in an office 3 days a week (Tuesday through Thursday). Candidates who do not live near an office will have a remote work arrangement, with the expectation of coming into an office as business needs arise.
Responsibilities
- 1) Knowledge Platform & Technology Ownership
- Own the knowledge platform technology roadmap, ensuring capabilities support both human and AI consumption (search, retrieval, API access, orchestration, analytics).
- Define and govern target-state knowledge as a service architecture for headless/hybrid CMS, knowledge delivery, and integration patterns across channels and AI services.
- Establish platform standards for content lifecycle, versioning, publishing workflows, and traceability to support regulated and high-risk knowledge domains.
- Partner with IT/Architecture to ensure platform decisions align with security, privacy, accessibility, resiliency, and enterprise integration standards.
- 2) Semantic & Content Model Leadership
- Collaborate with Sr. Content Architect to lead the design and evolution of content models (structured, modular, reusable components) and a supporting semantic layer (metadata, taxonomy, entity relationships) to normalize, classify, and define rules for platform-agnostic, AI-safe content.
- Define best practices for field enforcement, content validation rules, and model governance (who can change what, how changes are tested, and how impacts are managed).
- Enable improved findability and retrieval quality by establishing standards for classification, tagging, synonyms, and relationships (e.g., product, policy, procedure, scenario, jurisdiction, audience).
- Guide Business Units in contributing domain models, metadata, and data assets into the enterprise ontology using defined governance and intake processes.
- 3) AI Enablement & Integration
- Partner with Sr. Consultant AI Content Strategy and engineering to define and execute strategy supporting the Enterprise Knowledge Team, ensuring content is structured and semantically enriched for consumption by LLMs, agentic systems, and automation platforms (e.g., Amazon Connect, Google Vertex AI)
- Ensure knowledge assets and platform capabilities integrate effectively with AI systems (e.g., retrieval-augmented generation, agent workflows, summarization, classification, routing).
- Partner with Sr. Consultant AI Content Strategy on content development pipeline
- Establish patterns for knowledge-to-AI pipelines: ingestion, transformation, chunking strategy, embedding refresh, and evaluation.
- Indexing and retrieval
- (vector + keyword + metadata filters)
- Grounding and citations
- (source traceability)
- Quality scoring
- (completeness, freshness, readability, accuracy signals)
- Guardrails
- (approved sources, access control, confidence thresholds)
- 4) Team Enablement & Capability Building
- Upskill knowledge managers, content strategists, authors, data science and technology staff in:
- Headless CMS fundamentals and architecture patterns
- Modular content design and structured authoring
- Content modeling practices (components, schemas, validations)
- Semantic tagging and governance
- Create playbooks, training modules, office hours, and “model review” forums to accelerate adoption and consistency.
- 5) Influence, Change Leadership & Stakeholder Alignment
- Serve as a trusted advisor, translating complex technical concepts into clear business outcomes and risk/reward tradeoffs.
- Influence leaders and teams who may be unfamiliar with structured content or skeptical of change-using data, prototypes, and outcome-based narratives.
- Drive cross-functional decisions and alignment across product owners, SMEs, operations, compliance/legal, and technology partners.
- Organize and facilitate working sessions, on-sites, and executive briefings that establish shared understanding of content architecture, semantic dependencies, and migration constraints.
- Proactively identify gaps where Knowledge Team involvement is missing from AI initiatives and advocate for inclusion.
- Partner with Business Units to understand domain concepts, terminology, operational data, and AI use cases, translating them into ontologically aligned knowledge data structures.
- 6) Governance, Risk, and Measurement
- Ensure auditability and defensibility for AI-enabled experiences by maintaining clear provenance and change history.
- REQUIRED QUALIFICATIONS:
- 10+ years in a combination of knowledge management, content strategy, information architecture, content engineering, or platform/product leadership—with senior-level ownership of cross-functional outcomes.
- Demonstrated experience designing and implementing headless or hybrid content management approaches and structured content models.
- Strong understanding of semantic concepts: taxonomy, ontology, metadata strategy, entity modeling, graphing, and governance.
- Proven experience partnering with Data Science / AI Engineering teams to integrate knowledge with AI/ML systems (RAG or similar patterns).
- Excellent executive communication skills—able to drive decisions, align stakeholders, and simplify complex technical tradeoffs.
- Strong influencing and change leadership skills, with a track record of moving resistant stakeholders toward adoption.
- PREFERRED QUALIFICATIONS:
- Master’s degree in information science, library science, UX design, Artificial Intelligence or a related field; or a bachelor’s degree with equivalent additional experience.
- Experience enabling or operating AI-powered knowledge solutions (e.g., LLM grounding, evaluation, content quality scoring, agent tool use).
- Experience with AI applications in content or taxonomy work, such as AI-assisted classification, metadata enrichment, prompt engineering, or knowledge graph development.
- A product-oriented mindset and consultative working style — you think about knowledge, information architecture and taxonomy as products with users and adoption strategies, and you drive outcomes through influence rather than authority.
- Familiarity with search and retrieval concepts (ranking, relevance, semantic search, hybrid search, vector search, metadata filtering).
- Experience in regulated environments requiring strong governance, auditability, and access controls.
- Background in content operations at scale (workflow design, editorial governance, QA processes).
- Familiarity with SQL, Python, JSON, SPARQL, RDF, OWL etc. is a plus.
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